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Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages…
This document presents the Center for Robot Assisted Search And Rescue - Uncrewed Aerial Systems - Disaster Response Overhead Inspection Dataset (CRASAR-U-DROIDs) for building damage assessment and spatial alignment collected from small…
Predictions of thunderstorm-related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard…
Humanitarian actions require accurate information to efficiently delegate support operations. Such information can be maps of building footprints, building functions, and population densities. While the access to this information is…
Following extreme events, efficient restoration of infrastructure systems is critical to sustaining community lifelines. During the process, effective monitoring and control of the infrastructure restoration progress is critical. This…
Holistic analysis of many real-world problems are based on data collected from multiple sources contributing to some aspect of that problem. The word fusion has also been used in the literature for such problems involving disparate data…
To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi-agent simulation model which enables integrated assessment of hazard, infrastructure system, and…
The mobility of residents and their access to essential services can be highly affected by transportation network closures that occur during and after coastal hazard events. Few studies have used geographic information systems coupled with…
We present a procedure for assessing the urban exposure and seismic vulnerability that integrates LiDAR data with aerial and satellite images. It comprises three phases: first, we segment the satellite image to divide the study area into…
Buying a home is one of the most important buying decisions people have to make in their life. The latest research on real-estate appraisal focuses on incorporating image data in addition to structured data into the modeling process. This…
In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas. Remote sensing has the potential to significantly reduce the time…
Earthquakes are one of the most destructive natural disasters harming life and the infrastructure of cities. After an earthquake, functioning communication and computational capacity are crucial for rescue teams and healthcare of victims.…
Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is…
Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as…
Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that they do not agree with ground-based measurements. An…
This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the United States Geological Survey (USGS) Landslide Inventory data…
This work discusses how to choose performance measures to compare numerical simulations of a flood event with one satellite image, e.g., in a model calibration or validation procedure. A series of criterion are proposed to evaluate the…
The analysis of natural disaster-related multimedia content got great attention in recent years. Being one of the most important sources of information, social media have been crawled over the years to collect and analyze disaster-related…
The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments current status through the use of deep learning framework on fine-grained traffic data. Predictive…
Unmanned aerial vehicle-assisted disaster recovery missions have been promoted recently due to their reliability and flexibility. Machine learning algorithms running onboard significantly enhance the utility of UAVs by enabling real-time…